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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Sodality: Jurnal Sosiologi Pedesaan MANAJEMEN IKM: Jurnal Manajemen Pengembangan Industri Kecil Menengah Jurnal Ilmu dan Teknologi Kelautan Tropis IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmu Sosial dan Humaniora Jurnal Kawistara : Jurnal Ilmiah Sosial dan Humaniora Journal of Indonesian Tourism and Development Studies JURNAL ELEKTRO Jurnal Perkotaan Jurnal Kebijakan dan Administrasi Publik AdBispreneur PAX HUMANA ARISTO JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komunikasi Kritis Humaniora MUWAZAH: Jurnal Kajian Gender Cakrawala Jurnal Penelitian Sosial Building of Informatics, Technology and Science Jurnal Mantik Journal of Information Systems and Informatics Jurnal Studi Sosial dan Politik Jurnal Teknik Informatika C.I.T. Medicom JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) EKONOMI, KEUANGAN, INVESTASI DAN SYARIAH (EKUITAS) Jurnal Sistem Komputer dan Informatika (JSON) JOURNAL OF BUSINESS AND ECONOMICS RESEARCH (JBE) Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Cita Ekonomika: Jurnal Ilmu Ekonomi ARBITRASE: JOURNAL OF ECONOMICS AND ACCOUNTING International Journal on Social Science, Economics and Art KLIK: Kajian Ilmiah Informatika dan Komputer International Journal of Basic and Applied Science Indonesian Journal of Tourism and Leisure Jurnal InterAct Jurnal Sosiologi Engagement: Jurnal Pengabdian Kepada Masyarakat JKAP (Jurnal Kebijakan dan Administrasi Publik) Jurnal Kawistara Muwazah: Jurnal Kajian Gender
Yerik Afrianto Singgalen
Universitas Katolik Indonesia Atma Jaya
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Physics Public Health Social Sciences Other

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Tourist Preferences at Hotel and Resort Based on Review Data Singgalen, Yerik Afrianto
Journal of Business and Economics Research (JBE) Vol 6 No 1 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jbe.v6i1.6844

Abstract

This study investigates the relationship between cultural dynamics and tourist preferences in hotel and resort settings through comprehensive review data analysis across multiple countries of origin. Using thematic analysis methodology implemented through Atlas.Ti software, the research examines patterns in accommodation preferences, service expectations, and satisfaction determinants. The findings reveal significant variations in guest preferences across different cultural backgrounds, with statistical analysis showing distinctive patterns in visitor demographics. Notably, couples constitute the highest proportion of visitors across multiple destinations, with exceptionally high concentrations in Hong Kong (92%), Malta (90%), and Argentina (62%). Cultural influences manifest through specific preferences in room configurations, dining experiences, and recreational offerings, where Asian tourists emphasize personalized service interactions and traditional elements. At the same time, European visitors prioritize authentic local experiences within luxury accommodation frameworks. The study identifies four key dimensions of managerial adaptation: cultural sensitivity, customization, staff training, and service modifications. These findings contribute to advancing understanding of cultural influences in hospitality contexts while providing practical guidelines for enhancing guest experiences in international hotel operations. The research concludes that effective cultural adaptation strategies enhance guest satisfaction and create sustainable competitive advantages in increasingly globalized hospitality markets.
IndoBERT-Based Sentiment Analysis for Understanding Hotel Guests’ Preferences Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 6 No 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i2.6864

Abstract

The rapid growth of the hospitality industry and the increasing reliance on online reviews emphasize the need for advanced sentiment analysis tools to understand customer preferences effectively. This study explores the application of IndoBERT, a pre-trained language model tailored for the Indonesian language, in classifying sentiments from hotel guest reviews. Utilizing a dataset of 715 reviews, the study employed the Knowledge Discovery in Databases (KDD) framework for systematic data preprocessing, feature extraction, and machine learning analysis. IndoBERT demonstrated exceptional performance, achieving perfect precision, recall, and F1-scores of 1.00 for both positive (657 reviews) and negative (53 reviews) sentiment classes. The ROC curve analysis also yielded a mean AUC score of 0.86, validating the model's robustness and reliability. The results highlight IndoBERT's capability to accurately capture linguistic nuances and contextual meaning, offering actionable insights into factors influencing guest satisfaction, such as cleanliness, staff behavior, and service quality. This research contributes to advancing natural language processing applications in regional contexts and provides practical implications for enhancing service strategies in the hospitality sector. Future research should expand the model's application to other industries and explore multimodal approaches for a more comprehensive understanding of customer behavior.
Improved Sentiment Classification Using Multilingual BERT with Enhanced Performance Evaluation for Hotel Guest Review Analysis Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 6 No 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i2.6870

Abstract

Sentiment analysis in hotel guest reviews has become essential for evaluating customer satisfaction and service quality. This study improves sentiment classification accuracy by utilizing the Multilingual BERT model with an improved performance evaluation framework. Using the Knowledge Discovery in Databases (KDD) methodology, this research involves data selection, preprocessing, transformation, sentiment classification, and performance evaluation. A dataset of 715 hotel reviews from Qubika Boutique Hotel, sourced from Agoda, was used to assess the model's effectiveness. The classification results showed high accuracy in identifying positive sentiment, with 98% precision, 97% memory, and 98% F1 score, as observed in 432 correctly classified reviews. However, challenges were identified in the classification of neutral sentiment, which achieved a precision of 87% with 127 correctly classified cases, and negative sentiment, where the accuracy was 92%, with 104 correctly identified reviews. The overlap in confidence scores, especially in the range of 0.4-0.6 between neutral and negative sentiment, highlights the need for improved contextual embedding and hybrid modeling techniques. The sentiment distribution analysis revealed that 60-70% of reviews were positive, 20-30% neutral, and 10-15% indicated dissatisfaction, underscoring the need for targeted service improvement. These findings provide valuable insights for data-driven decision-making in hospitality management, enabling businesses to strengthen service power and address critical areas of concern. Future research should focus on refining model interpretability, expanding multilingual datasets, and integrating real-time sentiment analysis to improve classification performance. Strengthening these aspects will contribute to a more robust and scalable sentiment analysis framework, ensuring greater precision in capturing the guest experience and optimizing service strategies in the hospitality industry.
Data-Driven Hospitality: Advanced Forecasting Models for Hotel Occupancy Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6611

Abstract

Accurate forecasting of hotel booking demand is essential for resource optimization, revenue maximization, and enhanced customer experience in the hospitality industry. This study evaluates the performance of three forecasting models, ARIMA, Prophet, and LSTM, using historical booking data to identify the most effective approach for predicting demand. The evaluation employed four key metrics: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-squared (R²), providing a comprehensive comparison. The results indicate that the LSTM model outperformed the others in prediction accuracy, achieving the lowest MAE (2.71) and MAPE (21.33%), demonstrating its strength in capturing complex patterns. However, its negative R-squared value (-0.20) suggests limitations in explaining overall data variance compared to ARIMA (0.51) and Prophet (0.50). The Prophet model excelled in seasonal decomposition but showed the highest MAPE (71.86%), while ARIMA delivered robust residual diagnostics, adhering well to model assumptions with consistent variance and randomness in residuals. The findings suggest that while LSTM is most effective for short-term forecasting, ARIMA and Prophet provide better interpretability and reliability for long-term trend analysis. A hybrid approach combining the strengths of all three models is recommended to enhance predictive accuracy and robustness. This study provides actionable insights for industry stakeholders seeking to improve decision-making processes and operational efficiency through advanced forecasting techniques.
Social Media Management System for Educational Promotion Singgalen, Yerik Afrianto; Kartikawangi, Dorien; Winayu, Birgitta Narindri Rara
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1052

Abstract

Educational institutions, particularly tourism study programs, face significant challenges in managing fragmented and inefficient social media promotion strategies that hinder student recruitment and weaken institutional visibility. These problems arise from inconsistent content delivery, lack of stakeholder coordination, and limited performance monitoring and analytics capacity. To address these challenges, this research employs the Rapid Application Development (RAD) methodology through four stages: Requirements Planning, User Design, Construction, and Cutover. The requirement planning phase involved gathering aspirations from all stakeholders within the study program to ensure alignment in designing creative and effective promotional content. The resulting system integrates automated content workflows, scheduling algorithms, demographic-based audience targeting, and real-time performance analytics. The findings indicate substantial improvements in resource efficiency, precision of outreach, enrollment conversion rates, and institutional branding consistency. This research provides a comprehensive framework for transforming academic promotional practices through digital system integration, specifically tailored to the operational needs of educational institutions.
Toxicity Score and Sentiment Classification of Backpacker Content Reviews using SVM enhanced by SMOTE Singgalen, Yerik Afrianto
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5961

Abstract

This research explores the dynamics of backpacker tourism in Indonesia by analyzing online content from various regions, including Bandung, Dieng, Borobudur, Ijen, Bromo, Tumpak Sewu, Malang, Banyuwangi, and Bali. Using the Digital Content Reviews and Analysis Framework, the study systematically processed user-generated content to assess sentiment and toxicity levels. The analysis revealed that while most interactions were non-toxic, there were occasional spikes in harmful language, particularly in the categories of profanity and identity attacks. For example, toxicity scores in Malang, Banyuwangi, and Bali averaged 0.06995, with peaks reaching 0.78207, underscoring the need for ongoing content moderation. In addition, the study employed a Support Vector Machine (SVM) model enhanced by SMOTE to handle class imbalance. The model achieved an accuracy of 82.64% and a recall rate of 97.39%, demonstrating its effectiveness in identifying positive cases with minimal false negatives. The AUC scores, ranging from 0.970 to 0.979, indicated strong discriminatory power. These findings highlight the potential of using machine learning models to analyze large-scale, imbalanced datasets in tourism-related research. Overall, this study provides valuable insights into traveler perceptions of Indonesia’s backpacker destinations, emphasizing the importance of context in understanding online discourse. The integration of toxicity analysis and SVM modeling offers practical implications for improving tourism management, content moderation, and promoting sustainable tourism practices.
BiLSTM-LSTM Hybrid Model with Glove Embeddings for Hotel Review Sentiment Analysis Singgalen, Yerik Afrianto
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6420

Abstract

This study presents an optimized approach to sentiment classification of hotel reviews using a hybrid deep learning architecture. The model proposed combines Bidirectional Long Short-Term Memory (BiLSTM) with LSTM networks, enhanced by pre-trained GloVe word embeddings and SMOTE-ENN for handling data imbalance. The architecture incorporates a BiLSTM layer with 64 units and an LSTM layer with 32 units, complemented by dense layers and dropout regularization for optimal performance. Experimental results demonstrate the effectiveness of our approach, achieving an accuracy of 89.47% and an AUC score of 0.9607. The model shows robust performance across positive and negative sentiments, with precision scores of 0.96 and 0.82, respectively. Integrating SMOTE-ENN for data balancing and GloVe embeddings significantly enhanced the model's ability to capture semantic relationships in text data. Our findings indicate that this hybrid approach effectively addresses the challenges of sentiment analysis in the hospitality domain, particularly in processing nuanced customer feedback. The high AUC score suggests strong discriminative capability, while the balanced precision-recall trade-off demonstrates the model's practical applicability for real-world hotel review analysis.
Performance Analysis of IndoBERT for Sentiment Classification in Indonesian Hotel Review Data Singgalen, Yerik Afrianto
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6505

Abstract

This study investigates the performance of a sentiment classification model leveraging IndoBERT to analyze Indonesian hotel review data. Sentiment analysis is crucial for extracting actionable insights from customer reviews, yet challenges such as linguistic diversity and imbalanced datasets complicate accurate classification. The dataset comprises 90% Positive, 5% Neutral, and 5% Negative sentiments, reflecting significant class imbalance. A fine-tuned IndoBERT model was trained over three epochs, with performance assessed using metrics such as accuracy, precision, recall, F1-score, confusion matrices, and ROC and Precision-Recall curves. The results indicate high global accuracy (92.52%) and robust performance for the Positive class (F1-score: 96.09%, AUC: 0.90). However, significant limitations were observed for minority classes, with the Neutral class achieving precision, recall, and F1-scores of 0.00, and the Negative class obtaining a low F1-score of 28.57%. These findings underscore the influence of dataset imbalance, where the dominance of the Positive class skews model predictions. Future research should explore techniques such as oversampling SMOTE, reweighting loss functions, or hybrid architectures to mitigate imbalance and improve performance across all sentiment categories. This research contributes to advancing sentiment classification methodologies for Indonesian text, offering practical implications for enhancing customer feedback analysis in the hospitality industry.
Digital Mapping of Fermented Foods for the Advancement of Gastronomy Tourism in Indonesia Singgalen, Yerik Afrianto; Kartikawangi, Dorien; Winayu, Birgitta Narindri Rara
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1055

Abstract

This research introduces a pioneering digital mapping framework for Indonesian fermented foods that integrates geospatial technologies with traditional gastronomic knowledge systems. Employing Rapid Application Development methodology on the Oracle APEX platform, the study establishes a comprehensive documentation infrastructure capturing the geographical distribution, production methodologies, and cultural significance of diverse fermentation practices across Indonesia's archipelagic landscape. The resulting prototype offers multifunctional capabilities through an intuitive interface design that serves preservation imperatives and tourism development objectives. Findings demonstrate that systematic digital documentation of fermented food traditions creates measurable economic opportunities through enhanced destination competitiveness, specialized culinary tourism routes, and improved market visibility for artisanal producers. The community-driven documentation protocols position local knowledge-holders as primary content contributors, while the system architecture establishes essential connections between geographical contexts and traditional fermentation techniques. This research addresses critical documentation gaps while establishing standardized protocols applicable beyond Indonesia to other regions with significant fermentation heritage. The digital mapping system ultimately functions as both a cultural preservation mechanism and a strategic asset for sustainable gastronomy tourism development, offering a replicable model for transforming endangered culinary knowledge into economically viable digital assets that benefit traditional food-producing communities.
Big Data in Tourism and Hospitality Industry: Predictive Analytics of Hotel Room Trends Singgalen, Yerik Afrianto
Indonesian Journal of Tourism and Leisure Vol 6, No 1 (2025)
Publisher : Lasigo Akademia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36256/ijtl.v6i1.474

Abstract

This study investigates predictive analytics applications in the hospitality sector, specifically employing the XGBoost algorithm to predict room selection patterns based on guest data. Analysis of 900 booking records revealed that three variables—"Length of Stay," "Rating," and "Guest Type"—exhibited the strongest predictive power for room preferences. The implementation achieved 85% classification accuracy, revealing subtle correlations between customer characteristics and accommodation choices. Our findings suggest that hotels can leverage similar analytical frameworks to refine inventory management strategies, develop targeted promotional campaigns, and streamline operational workflows. The investigation also identified methodological limitations regarding class distribution in the dataset, suggesting that enhanced feature selection techniques could potentially reduce error rates in subsequent modeling approaches. This work contributes to the growing body of evidence demonstrating how advanced data analytics can drive competitive advantage and sustainability initiatives within tourism enterprises.
Co-Authors A.Y. Agung Nugroho Agnes Harnadi Agnes Harnadi Agung Mulyadi Purba Alfonso Harrison Aloisius Gita Nathaniel Astuti Kusumawicitra Astuti Kusumawicitra Astuti Kusumawicitra Laturiuw Astuti Kusumawicitra Laturiuw Bernardus Alvin Rig Bernardus Alvin Rig Biafra Daffa Farabi Biafra Daffa Farabi Billy Macarius Sidhunata Brito, Manuel Charitas Fibriani Christanto, Henoch Juli Christine Dewi Danny Manongga Dasra, Muhamad Nur Agus Eko Sediyono Eko Widodo Elfin Saputra Elfin Saputra Elly Esra Kudubun Fang, Liem Shiao Faskalis Halomoan Lichkman Manurung Gatot Sasongko Gilberto Dennis G E Sidabutar Gintu, Agung Rimayanto Gudiato, Candra Henoch Juli Christanto Henoch Juli Christanto Heru Prasadja Heru Prasadja, Heru Hindriyanto Dwi Purnomo Hironimus Cornelius Royke Irene Sonbay Irwan Sembiring Jesslyn Alvina Seah Jonathan Tristan Santoso Juli Christanto, Henoch Kartikawangi, Dorien Kusumawicitra, Astuti Manuel Brito Marthen Timisela Mavish, Steven Michael Kenang Gabbatha Nantingkaseh, Alfonso Harrison Nicolas Arya Nanda Susilo Nugroho, A. Y. Agung Octa Hutapea Octa Hutapea Pamerdi Giri Wiloso Pamerdi Giri Wiloso Pamerdi Giri Wiloso, Pamerdi Giri Pedro Manuel Lamberto Buu Sada Pinia, Nyoman Agus Perdanaputra Pontolawokang, Theresya Ellen Pristiana Widyastuti Pristiana Widyastuti Purwoko, Agus Puspitarini, Titis Radyan Rahmananta Radyan Rahmananta Rafael Christian Rahadi, Abigail Rosandrine Kayla Putri Rahmadini, Asyifa Catur Richard Emmanuel Adrian Sinaga Rosdiana Sijabat Samuel Piolo Seingo, Martha Maraka Setiawan, Ruben William Siemens Benyamin Tjhang Sri Yulianto Joko Prasetyo Stephen Aprius Sutresno, Stephen Aprius Suharsono SUHARSONO Suni, Eugenius Kau Tabuni, Gasper Tharsini, Priya Timisela, Marthen Titi Susilowati Prabawa Titis Puspitarini Widodo, Eko Winayu, Birgitta Narindri Rara Yan Dirk Wabiser Yoel Kristian Zsarin Astri Puji Insani